Tutorial 43-Random Forest Classifier and Regressor
Summary
TLDRIn this YouTube video, Krishna explores the concept of random forests, a machine learning technique that utilizes bagging with decision trees. He explains how random forests work by creating multiple decision trees using row and feature sampling, which helps in reducing variance and improving model accuracy. Krishna highlights the difference between using a single decision tree, which can lead to overfitting, and the ensemble approach of random forests that results in lower bias and variance. He also touches on the use of majority voting for classification and averaging/median for regression in random forests, emphasizing their effectiveness in various machine learning applications.
Takeaways
- 🌳 The video introduces Random Forests, a machine learning technique that uses an ensemble of decision trees.
- 🔄 Random Forest is a type of bagging technique, which involves creating multiple models to improve accuracy and control overfitting.
- 🌱 The base learner in a Random Forest is the decision tree, and multiple decision trees are used to form the forest.
- 🔢 The script explains how Random Forests handle both classification and regression problems, using majority voting for classification and averaging/median for regression.
- 🔄 The process involves random sampling with replacement for both rows and features, which helps in creating diverse decision trees.
- 📉 The video highlights that decision trees can suffer from high variance, but Random Forests mitigate this by combining multiple trees through majority voting.
- 🔑 The script emphasizes the importance of hyperparameters, particularly the number of decision trees, in tuning a Random Forest model.
- 💡 Random Forests are robust to changes in the dataset because of the random sampling of rows and features, leading to lower variance in predictions.
- 🏆 The video mentions that Random Forests are a favorite algorithm among developers and work well for most machine learning use cases.
- 📈 The video concludes with a call to action for viewers to subscribe, share, and engage with the content for more learning opportunities.
Q & A
What is the main topic discussed in Krishna's YouTube video?
-The main topic discussed in Krishna's YouTube video is Random Forests, which is a bagging technique used in machine learning for both classification and regression tasks.
What is bagging and how does it relate to random forests?
-Bagging, or Bootstrap Aggregating, is a technique where multiple models are built on different subsets of the original dataset and then aggregated to improve the stability and accuracy of the model. Random forests use this technique by building multiple decision trees on different subsets of the data and then aggregating their predictions.
How does row sampling with replacement work in the context of random forests?
-Row sampling with replacement in random forests involves selecting a subset of rows from the dataset for training each decision tree. This process is repeated with replacement, allowing the same row to be selected more than once, which helps in creating diverse subsets for each tree.
What is feature sampling with replacement and why is it used in random forests?
-Feature sampling with replacement is the process of selecting a subset of features from the dataset for training each decision tree. This is used in random forests to further diversify the training data for each tree, which helps in reducing the variance of the model.
Why are decision trees used as the base learner in random forests?
-Decision trees are used as the base learner in random forests because they are easy to interpret, handle non-linear relationships well, and can be easily combined using majority voting for classification or averaging for regression.
What is the role of D and D- in the context of random forest training?
-In the context of random forest training, D represents the total number of records in the dataset, and D- represents the number of records in the sample used to train each decision tree. D- is always less than D because only a subset of the records is used for training each tree.
How does random forest handle the high variance problem associated with individual decision trees?
-Random forests handle the high variance problem by using multiple decision trees and aggregating their predictions through majority voting for classification or averaging for regression. This ensemble approach reduces the overall variance of the model.
What is the significance of majority voting in the context of random forest classifiers?
-Majority voting in random forest classifiers is a method of aggregation where the final prediction is made based on the most common prediction among all the decision trees. This helps in reducing the impact of any single tree's prediction and improves the overall accuracy of the model.
How does random forest handle regression problems?
-In regression problems, random forests handle the output by calculating the mean or median of the continuous values predicted by each decision tree. The choice between mean and median depends on the distribution of the output values.
Why are random forests popular among machine learning practitioners?
-Random forests are popular among machine learning practitioners because they tend to perform well on a variety of datasets, are less prone to overfitting, and can handle both classification and regression tasks effectively. They also provide a good balance between bias and variance.
What is the importance of hyperparameters in tuning a random forest model?
-Hyperparameters in random forests, such as the number of decision trees, are crucial for tuning the model's performance. The right balance of hyperparameters can lead to better generalization and improved accuracy on unseen data.
Outlines
🌲 Introduction to Random Forests and Bagging
In this paragraph, Krishna introduces the topic of random forests and explains that they are an extension of the bagging technique, which was discussed in a previous video. Random forests utilize decision trees, and Krishna walks through how a dataset is used in this model. The dataset is split into subsets through row and feature sampling, which is then fed to multiple decision trees. Krishna emphasizes that the process involves sampling with replacement, ensuring varied decision trees are trained on different portions of the data.
📊 Low Bias and High Variance in Decision Trees
This paragraph dives deeper into the concepts of low bias and high variance, especially when decision trees are grown to their full depth. Krishna explains that while training data performs well (low bias), decision trees can overfit and show high variance with new test data. To mitigate this, random forests use multiple decision trees and combine their outputs using a majority vote. By using this ensemble technique, random forests convert high variance into low variance, leading to better predictions and accuracy.
🎯 Accuracy and Robustness of Random Forests
Here, Krishna highlights the robustness of random forests, explaining that changing a portion of the data doesn't significantly impact the model's performance due to the distributed nature of row and feature sampling. He explains how random forests maintain low variance, ensuring consistent accuracy even when test data changes. The paragraph emphasizes how random forests, due to their design, handle machine learning tasks effectively, and this makes them a favorite algorithm for many developers.
🔧 Differences Between Classification and Regression in Random Forests
In this paragraph, Krishna explains the differences between using random forests for classification and regression. For classification tasks, the output is based on a majority vote, whereas for regression tasks, the average or median of the decision trees’ outputs is used. He also touches on hyperparameters, specifically how the number of decision trees can be optimized for performance. This paragraph rounds off the explanation of how random forests are used for both tasks and provides insights into how these models can be fine-tuned.
📢 Conclusion and Call to Action
Krishna concludes the video by encouraging viewers to subscribe to the channel and share the video with anyone who might benefit from it. He emphasizes that all the materials presented are free to share and expresses his gratitude to the viewers. Krishna wraps up by wishing everyone a great day and promising more informative content in future videos.
Mindmap
Keywords
💡Random Forest
💡Bagging
💡Decision Tree
💡Row Sampling
💡Feature Sampling
💡Overfitting
💡High Variance
💡Low Bias
💡Majority Vote
💡Regression
Highlights
Introduction to Random Forests as an extension of bagging techniques.
Random Forests use multiple decision trees to enhance model accuracy and reduce overfitting.
Explanation of row sampling and feature sampling with replacement in Random Forests.
The process of training each decision tree using different samples of rows and columns.
Importance of combining multiple decision trees for reducing high variance in the model.
Decision trees, when used individually, tend to have low bias and high variance.
The role of majority voting in Random Forest classifiers for binary classification problems.
For regression problems, Random Forests use the mean or median of decision tree outputs instead of majority voting.
Random Forests can handle changes in data more robustly due to the row and feature sampling approach.
Random Forests convert high variance into low variance by aggregating results from multiple decision trees.
Decision trees trained to their complete depth may lead to overfitting in some scenarios.
Row and feature sampling allow decision trees to specialize in specific parts of the dataset.
Random Forests generally provide high accuracy across various machine learning tasks.
The importance of tuning hyperparameters, such as the number of decision trees in Random Forest models.
The concept that Random Forests are highly effective for both classification and regression tasks.
Transcripts
hello all my name is Krishna and welcome
to my youtube channel today we are going
to discuss about random forests now in
my previous video I have already put up
a video on bagging and I told you that
one of the technique that is basically
mostly used is something called as
random forest so random forest
classifier or a regresar is basically a
bagging technique and we are going to
discuss both of them in this particular
session so let me just consider and let
me just show you some example suppose I
have a data set now how does random
forest basically work suppose this is my
data set D now I told you that in
bagging we basically have many based
learners based learning models so this
suppose this is my m1 model this is my
m2 model this is my m3 model and many
more models like this okay so suppose
this is my MN model now when we are
designing this particular model in the
random forest this model is basically
called as decision trees we are going to
use decision trees in this model and as
I had explained in the bagging technique
suppose in this particular data set we
have the D records okay D number of
records and M number of columns
suppose I have that many columns n
number of columns so what we do is that
from this particular data set we will be
picking up some sample of rows and some
sample of features okay
so initially I will pick up some sample
of rows I will say it as row sampling
row sampling with replacement I just say
what is that particular placement term
means so I'm going to take some rows
from this particular data set and
similarly I am going to pick up some
columns okay or I can also write this as
feature sample so FS okay feature sample
replacement now that is how backing
words works right we will be taking some
amount of rows given to our decision
tree one so this is really a decision
tree one decision tree two three four
and okay so all this decision tree
suppose I say that this particular data
set is basically D - I always remember
when I say D - D - is always less than D
because the number of Records from here
I'm just taking a sample of Records and
suppose if I consider that I have taken
and suppose small D - rows and n columns
right n number of features so always
remember this M will always be greater
than n
and this D - this capital D will always
be greater than D - or small D I'll say
it as small D because the total number
of Records I have written as D okay so
always remember that guys I am going to
take some number of rows some number of
features give it to my decision tree one
this decision tree one will get trained
on this particular data set now
similarly from a decision tree to what
I'll do is that again this row sampling
will happen with replacement now what
does with replacement mean is that oh
here suppose from this particular record
I have some of the records some of the
records it may come into this particular
scenario so when I am doing row sampling
with replacement not all the records
will get replayed repeated but instead
I'll be taking another sample of records
and give to our decision tree - so when
I'm doing again a row sampling + feature
sampling over here it may be it may
happen that some of the records may get
repeated here some of the features and
get repeated here but we are at least
changing many records again we are doing
this row sampling okay row sampling and
feature sampling so suppose in this
particular case I had given feature one
two three four five suppose in this
particular case I will give other
features like feature 1 3 4 5 6 7 like
that and similarly that row sampling
also happens in the similar way now
after doing this row sampling and future
sampling I will give this particular
records to my decision tree - this will
get trained on this particular data
similarly for every decision tree this
thing is going to happen where you are
going to perform row sampling and
feature sampling ok row sampling and
feature sampling now this decision tree
gets trained on this particular data ok
and now it will be able to give them
accuracy or it will be able to give the
prediction now the next thing is that
whenever I get my test data whenever I
get my test data suppose I am giving one
record of the test data into this
particular decision tree one suppose
decision tree one suppose I am
considering a binary classification
problem decision tree once gives me one
this also gives me one this gives me 0
and suppose this gives me 1 okay now
when we see over here finally we know
that this is my bootstrap and now
according to the bagging finally after
aggregate it right so for aggregating I
am going to use the majority would now
when I use the majority would I know
that the max
of models that has basically st. output
is like one so away I can see one two
three models is basically saying it as
one so finally my output is basically
one now this is how a decision random
forest basically works the based learner
is decision tree now you need to
understand one more thing in this what
is happening if when we are using many
decision trees in this particular random
forest because you should know that
decision tree whenever I use decision
tree it has two properties suppose if I
am creating a decision tree to its
complete depth so when I do that it
basically has low bias and high variance
I'm going to explain about what is low
bias and high variance just let me write
it down for Stauffer so low bias
basically says that if I am creating my
decision tree to its complete depth then
what will happen is that it will get
properly trained for our training data
set okay so the training error will be
very very less high violence high
variance basically says that now
whenever we get our new test data those
decision tree they are prone to give
larger amount of errors so that is
basically called as high variance okay
so in short whenever we are creating the
decision tree to its complete depth it
leads to something called as overfitting
okay so now what is happening in random
forests in random forests I am basically
using multiple decision tree right and
we know that each and every decision
tree will be having high variance right
but when we combine all the decision
tree with respect to this majority vote
what will happen is that this high
variance will get converted into low
variance because now when we are using
row sampling and feature sampling and
giving the records to the decision tree
the decision tree tends to become an
expert with respect to this specific
rows or the data set that they have okay
since we are giving different different
records to each and every decision tree
they become an expert with respect to
those records they get trained on that
particular data specifically and in
order to convert this high variance to
low variance we are basically taking the
majority vote okay we are not just
depending
on one decision tree output so because
of that this high variance will get
converted into low variance when we are
combining multiple decision til now one
more advantage you need to understand
suppose i have thousand records over
here now in this thousand records okay
suppose I just changed let me just
change two hundred records will this
change of the data impact this random
forest now understand guys we are doing
random sampling sorry rose sampling and
feature sampling for each and every
decision tree now if I just change to
one hundred records now this two hundred
records will be properly splitted
between this data decision tree so when
it is actually splitted then what will
happen is that some of the number of
roles or some of the number of records
will go to decision tree one then
decision tree two then three then four
so this data change will also not make
that much impact to a decision tree with
respect to the accuracy or with respect
to the output so that is why this high
variance
even though whenever we change our data
whenever we change our test data we will
be getting a low variance error or our
error rate will be very very low our
accuracy will be very very good since we
are taking the majority of what we are
doing row sampling and feature sampling
giving to the decision trees now this is
the most important property of random
forests so random forest actually works
very well with respect to most for the
machine learning use cases that you are
basically trying to do and I've seen in
most of the companies developer have
made the favorite favorite algorithm as
random forest let it be classifier
aggressor one more point I missed out is
that suppose if this is not a binary
classification it is a regression
problem what will happen now this
particular decision tree suppose it
gives me a continuous value this also
gives me a continuous value this also
gives me a continuous value for that
what we do is that in the regression
problem we either take the mean of all
this particular output or the median of
that particular output it depends on the
distribution of the output how the
decision tree is basically given so
usually the main random forest that
works with respect to a scale on it
tries to find out the average of this
particular output from all the decision
tools and that is much a simple it
you need to understand if I just use
single decision tree it will have low
bias and high variance if I want to
convert this high by high variance into
low variance I have to basically use
multiple decision tree apart from that I
also have to use row sampling and
feature sampling so that I will be able
to convert that into low variance that
basically is our accuracy for the new
data or the test data will be very very
good so this was all about random
forests and I have explained you both
about classifier and regressor only the
difference between classifier and
regression is that classify uses
majority wood I'll just write it down
majority wood whereas in the case of
regression it will actually find out the
mean or the median of the particular
output of all the decision trees now the
hyper parameter that you have to
basically work on in that how many
decision trees you have to actually use
for the random forest okay how many
decision trees you have to basically use
so by with the help of hyper parameter
you'll be able to work that out okay so
this was all about the video of random
forest classifier and regression I hope
you like this particular video please
make sure you subscribe the channel
share with all your friends please share
with all your friends whoever require
this kind of health because all the
materials over here are free to share
with as much as people as you can
I'll see you all in the next video have
a great day thank you one and all
Weitere ähnliche Videos ansehen
All Learning Algorithms Explained in 14 Minutes
Project 06: Heart Disease Prediction Using Python & Machine Learning
Xgboost Regression In-Depth Intuition Explained- Machine Learning Algorithms 🔥🔥🔥🔥
Insurance Fraud Detection using Machine Learning | 11 ML Algorithms Used to Identify Insurance Fraud
Lec 04-Introduction to AI Algorithms
Machine Learning Interview Questions | Machine Learning Interview Preparation | Intellipaat
5.0 / 5 (0 votes)